JP6453299B2 - Methods for treating and diagnosing systemic lupus erythematosus - Google Patents

Description

This application claims the priority of US Provisional Application No. 61 / 792,284, filed Mar. 15, 2013, which is hereby incorporated by reference in its entirety.

  Systemic lupus erythematosus (SLE) is an autoimmune disease characterized by the production of abnormal autoantibodies in the blood. These autoantibodies bind to their respective antigens to form immune complexes that circulate and eventually deposit in the tissue. This immune complex deposition causes chronic inflammation and tissue damage.

  The exact reason for the abnormal autoimmunity that causes lupus is unknown. The inherited genes, viruses, ultraviolet light, and drugs can all play a role. Genetic factors increase the tendency to develop autoimmunity, and autoimmune diseases such as lupus, rheumatoid arthritis, and immune thyroid disorders occur more frequently in relatives of patients with lupus than in the general population. Some scientists believe that the immune system in lupus is more easily stimulated by external factors such as viruses or ultraviolet light. Sometimes lupus symptoms can be caused or exacerbated by brief exposure to the sun.

Patients with SLE may have a wide variety of symptoms and different combinations of organ lesions, and a single examination cannot confirm a diagnosis of SLE. Eleven criteria were established by the American College of Rheumatology to help physicians improve the diagnostic accuracy of SLE. These 11 criteria are closely related to the various symptoms seen in patients with SLE. A diagnosis of SLE is strongly suggested when the patient has 4 or more of these criteria. However, patients suspected of having SLE may never present sufficient criteria for a definitive diagnosis. Another patient shows sufficient criteria with just a few months or years of observation. Nonetheless, patients with only some of these conventional criteria in certain situations can be diagnosed with SLE. In these patients, some may later show other criteria, but many do not. The 11 criteria that are commonly used to diagnose SLE are:
1-cheek rash or “butterfly” rash covering the face cheek 2-discoid skin rash: patchy redness that can cause scar 3-photosensitivity: skin rash in response to sun exposure 4-mucosal ulcer: Oral, nasal, or throat intima ulcer 5-arthritis: swollen tender joints in two or more extremities 6-pleurisy / pericarditis: inflammation of the intimal tissue around the heart or lungs, usually chest pain during breathing 7- Renal abnormalities: Abnormal amount of urine protein, or agglomeration of cellular elements called cylinders 8- Brain inflammation: Appearing as seizures and / or mental abnormalities 9- Abnormal blood cell counts: white blood cells, red blood cells Or platelet reduction 10-immunological disorders: anti-dsDNA antibody, anti-Sm (Smith) antibody, syphilis blood test false positive, anticardiolipin antibody, lupus anticoagulant, or positive LE cell test But 11-antinuclear antibodies mentioned: Positive ANA antibody test.

  While these criteria serve as effective reminders of these features that distinguish lupus from other related autoimmune diseases, they are inevitably error prone. The determination of the presence or absence of a standard often requires interpretation. If rough criteria are applied to determine the presence or absence of signs or symptoms, patients can easily be diagnosed as having lupus even if they do not actually have lupus. Similarly, the range of clinical findings in SLE is considerably wider than described in the 11-item criteria, and each finding can vary in activity and severity levels from patient to patient. The continual change of SLE symptoms over the course of the disease also complicates difficult diagnosis. New symptoms can occur over time in previously unaffected organs. Traditionally, lupus is often misdiagnosed because there is no definitive examination of lupus.

  Monitoring disease activity is also problematic in treating patients with lupus. Lupus progresses in the acute phase of a series of redness or disease, followed by a remission phase. Symptoms of redness vary considerably between patients and even within the same patient, including fatigue, fever, symmetric joint pain, and photosensitivity (onset of redness after short-term sun exposure). Other symptoms of lupus include inflammation of the heart and lung intima that causes hair loss, mucosal ulcers, and chest pain.

  Red blood cells, platelets, and white blood cells can be targeted in lupus, causing anemia and bleeding disorders. Even more severely, immune complex deposition and chronic inflammation in the blood vessels can cause kidney damage and sometimes renal failure requiring dialysis or kidney transplantation. Because blood vessels are a major target of autoimmune reactions in lupus, extrasystolic attacks and heart disease are not uncommon. But over time, these flare-ups can cause irreversible organ damage.

In a first aspect, the invention is a method for treating a subject at risk for systemic lupus erythematosus (SLE) comprising:
(A) calculating a subject's SLE risk score based on the respective amount of each of the following primary markers in a biological sample obtained from a subject at risk of having SLE;
(I) red blood cell C4d (EC4d) marker,
(II) a B cell C4d (BC4d) marker, and (III) an antinuclear antibody (ANA),
Where the subject is at risk of having SLE, and the subject is negative for SLE based on individual amounts of EC4d marker, BC4d marker, anti-Smith (anti-Sm) antibody, and double-stranded DNA antibody (anti-dsDNA) And
(B) treating the subject for SLE or another disease based at least in part on the SLE risk score.

In another aspect, the invention is a method for diagnosing the likelihood that a subject at risk for SLE has SLE, comprising:
(A) Determine that subjects at risk for SLE are negative for SLE based on individual amounts of EC4d marker, BC4d marker, anti-Smith (anti-Sm) antibody, and double-stranded DNA antibody (anti-dsDNA) And
(B) calculating an SLE risk score based on the amount of each of the following primary markers in a biological sample obtained from a subject at risk of having SLE;
(I) red blood cell C4d (EC4d) marker,
(II) a B cell C4d (BC4d) marker, and (III) an antinuclear antibody (ANA),
A method is provided wherein the risk score indicates the likelihood that the subject has SLE.

  In one embodiment of the method, the method is further selected from the group consisting of an SS-B (La) marker, a Scl-70 marker, a Jo-1 marker, and a CENP marker in a biological sample obtained from the subject. Measuring the amount of one, two, three, or all four excluded markers, wherein the calculation of the SLE risk score is based on the amount of each of the primary marker and the one or more excluded markers. Including calculating.

  In another embodiment, the method further comprises determining the amount of MCV marker in a biological sample obtained from the subject, wherein the calculation of the SLE risk score is a primary marker, one or more exclusion markers, and Calculating an SLE risk score based on the respective amount of the MCV marker.

  In a further embodiment, the calculation of the SLE risk score includes adjusting one or more amounts of the primary marker by one or more transformation analyses. In one such embodiment, the one or more transformation analyzes include a logistic regression analysis, wherein the logistic regression analysis (i) adjusts the amount of each primary marker to create a weighted score for each primary marker. And (ii) combining the weighted scores of each primary marker to create an SLE risk score.

  In another embodiment, the calculation of the SLE risk score includes adjusting the amount of 1, 2, 3, or all four exclusion markers by one or more transformation analyses. In one such embodiment, the one or more transformation analysis comprises a logistic regression analysis, wherein the logistic regression analysis comprises: (i) a respective amount of primary markers and 1, 2, 3, or all four types of exclusion markers. Adjusting the amount of each to create a weighted score for the primary marker and all 1, 2, 3, or 4 types of exclusion markers; (ii) each primary marker and 1, 2, 3, or 4 types Combining the weighted scores of all exclusion markers to create a SLE risk score.

  In a further embodiment, the SLE risk score is determined by: (a) determining the natural logarithm of the net average fluorescence intensity of EC4d and BC4d as measured by fluorescence activated cell sorting (FACS); and (b) ANA at a predetermined threshold. To determine whether it is negative (value is 0), moderately positive (value is 1), or strong positive (value is 2), (c) any of the exclusion markers is positive above a certain threshold Determining whether (value is 1) or any of the exclusion markers does not exceed a predetermined threshold and all are negative (value is 0), and (d) in (a)-(c) Calculated by combining the determined values to obtain an SLE risk score.

FIG. 4 is a flow chart for SLE diagnosis based on the amount in a blood sample of a cell-based complement activation product comprising two detection layers, illustrating an embodiment of the method of the invention. It is a graph which shows the index score of anti- dsDNA negative object. RA: rheumatoid arthritis, PM / DM: polymyositis / dermatomyositis, Scl: scleroderma, SS: Sjogren's syndrome, NHV: healthy volunteers.

Detailed Description of the Invention All references cited herein are incorporated by reference. All embodiments disclosed herein can be combined with one or more other embodiments in the same or different aspects of the invention, unless the context clearly indicates otherwise.

  Unless otherwise required by context, throughout the description and claims, the terms “comprise,” “comprising,” and similar terms are intended to be inclusive, contrary to the exclusive or exhaustive meaning. Should be understood in any sense, that is, meaning "including but not limited to". Terms using the singular or plural number also include the plural or singular number respectively. In addition, the terms “herein”, “above”, and “below” and like meaning terms, as used in this application, refer to the application as a whole, It is not for a specific part of this application. As used herein, the singular forms “a”, “a”, and “the” include plural referents unless the context clearly indicates otherwise. As used herein, “and” is used interchangeably with “or” unless expressly stated otherwise.

  The description of the embodiments of the present disclosure is not intended to be exhaustive or to limit the disclosure to the precise form disclosed. Specific embodiments or examples of the present disclosure are described herein for purposes of illustration, and various equivalent modifications are possible within the scope of the present disclosure, as those skilled in the relevant art will recognize. . For example, method steps or functions may be presented in a predetermined order, but alternative embodiments may perform functions in a different order, or functions may be performed almost simultaneously. The teachings of the present disclosure set forth herein can be applied to other processes or methods as needed. Various embodiments described herein can be combined to provide further embodiments.

In a first aspect, the invention is a method for treating a subject at risk for systemic lupus erythematosus (SLE) comprising:
(A) calculating a subject's SLE risk score (also referred to herein as an “index score”) based on the amount of each of the following primary markers in a biological sample obtained from a subject at risk of having SLE: When,
(I) red blood cell C4d (EC4d) marker,
(II) a B cell C4d (BC4d) marker, and (III) an antinuclear antibody (ANA),
Where the subject is at risk of having SLE, and the subject is negative for SLE based on individual amounts of EC4d marker, BC4d marker, anti-Smith (anti-Sm) antibody, and double-stranded DNA antibody (anti-dsDNA) And
(B) treating the subject for SLE or another disease based at least in part on the SLE risk score;
Providing a method.

In a second aspect, the invention is a method for diagnosing the likelihood of SLE in a subject at risk for SLE, comprising:
(A) Determine that subjects at risk for SLE are negative for SLE based on individual amounts of EC4d marker, BC4d marker, anti-Smith (anti-Sm) antibody, and double-stranded DNA antibody (anti-dsDNA) And
(B) calculating an SLE risk score based on the amount of each of the following primary markers in a biological sample obtained from a subject at risk of having SLE;
(I) red blood cell C4d (EC4d) marker,
(II) a B cell C4d (BC4d) marker, and (III) an antinuclear antibody (ANA),
Here, a method is provided wherein the risk score indicates the likelihood that the subject has SLE.

  The methods of the present invention provide improvements in the diagnosis and treatment of systemic lupus erythematosus (SLE) compared to prior art methods. In one embodiment of any aspect, the method further comprises a Sjogren's syndrome type B antigen (SS-B (La)) marker, a Scl-70 marker (topoisomerase I), Jo- in a biological sample obtained from the subject. Determining the amount of one or more exclusion markers selected from the group consisting of one marker and a centromere B protein (CENP) marker (eg, 1, 2, 3, or all four), and an SLE risk score Calculating includes calculating an SLE risk score based on the respective amounts of the primary marker and the one or more exclusion markers. In this embodiment, the specificity of the SLE diagnosis is greatly improved compared to prior art methods.

  The marker can be any suitable marker, such as an antibody in a biological sample against one or more exclusion markers. For example, the Jo-1 marker can be an anti-Jo-1 antibody, the SS-B (La) marker can be an anti-SS-B (La) antibody, and the Scl-70 marker can be an anti-Scl-70 antibody. And the CENP marker can be an anti-CENP antibody. In further embodiments, the exclusion marker may further comprise an MCV (mutant citrullinated vimentin) marker, such as an anti-MCV antibody.

  Probes and assays for measuring the amount and normal amount of each of these markers in various tissue samples are known to those skilled in the art, and a wide variety of kits for making such measurements. Available from the manufacturer, including but not limited to those mentioned in the examples below.

  In another embodiment of any aspect, the calculation of the SLE risk score includes adjusting one or more amounts of the primary marker by one or more transformation analyses. Any such transformation analysis can be used, including but not limited to logistic regression analysis. In such an embodiment, the logistic regression analysis includes (i) adjusting the amount of each primary marker to create a weighted score for each primary marker; and (ii) combining the weighted score for each primary marker. Generating an SLE risk score.

In another embodiment, the SLE risk score is calculated by one or more transformation analyzes of the amount of one or more primary markers and the amount of one or more (1, 2, 3, or all four) excluded markers. Including adjusting. In such embodiments, the one or more conversion analyzes may include (i) adjusting the amount of each of the primary markers and the amount of each of the one or more exclusion markers to determine the primary marker and the one or more exclusion markers. A logistic regression analysis can be included that includes creating a weighted score and (ii) combining the weighted score of each primary marker and one or more exclusion markers to create an SLE risk.

  In such embodiments, the average fluorescence intensity (eg, output from the flow cytometer) is measured for (EC4d) and (BC4d) and logarithmized. “ANA20” refers to a positive rate, where ANA20 = 1 when the threshold of ANA units per mL of sample is exceeded, and ANA20 = 0 when the unit per mL is below the threshold. As will be appreciated by those skilled in the art, the “threshold” for determining whether a marker is “positive” or not should not vary by manufacturer / assay, but can be readily determined by one skilled in the art. . “Exclusion” is determined as 1 if the assay is positive for any “exclusion” marker and 0 if all “exclusion” markers are negative.

  In one embodiment, the SLE risk score is determined by: (a) determining the natural logarithm of net average fluorescence intensity (measured by fluorescence activated cell sorting (FACS)) for EC4d and BC4d; Determining whether negative (value is 0), moderately positive (value is 1), or strong positive (value is 2) based on a threshold (such as the threshold set by the kit manufacturer or end user) (C) whether any of the exclusion markers is positive (value is 1) above a predetermined threshold, or is any negative (value is 0) without any of the exclusion markers exceeding the predetermined threshold And (d) can be calculated by combining the values determined in (a)-(c) to create an SLE risk score, optionally one or more by one or more transformation analysis And adjusting the amount of the marker.

  In a further embodiment, determining the amount of BC4d marker comprises determining the amount of BC4d on the surface of B lymphocytes, and measuring the amount of EC4d marker comprises determining the amount of EC4d on the surface of red blood cells. For example, determining the amount of BC4d marker can include determining the amount of BC4d in a cell or tissue extract containing B lymphocytes, and determining the amount of EC4d marker includes extracting tissue in a cell or containing red blood cells. Determining the amount of EC4d in the object can be included. In a further embodiment, the amount of BC4d marker and the amount of EC4d marker are determined using antibodies specific for C4d as known in the art.

  The present invention provides an improved method for diagnosing and treating subjects at risk for systemic lupus erythematosus (SLE) that is considered negative based on an initial assessment using the amount of individual markers.

  The subject is preferably a human subject (adult or child). SLE is an autoimmune disease characterized by the production of abnormal autoantibodies in the blood. These autoantibodies bind to their respective antigens to form an immune complex that circulates and eventually deposits in the tissue. Subjects at risk for SLE can cause, but are not limited to, malaise, fever, chronic inflammation, tissue damage; cheek rash or “butterfly” rash covering the facial cheeks; discoid skin rash, ie, scarring Spotted redness; photosensitivity, skin rash in response to sun exposure; mucosal ulcers, ie, ulcers in the mouth, nasal cavity, or throat; arthritis, ie, two or more swollen tender joints in the extremities; pleurisy / heart Membrane inflammation, i.e. inflammation of the intimal tissue around the heart or lungs, chest pain during breathing; hair loss; abnormalities of the kidneys, i.e. abnormal mass of urinary proteins or clusters of cellular elements called casts; It may have symptoms including convulsions) and / or psychiatric abnormalities; abnormal blood counts; immunological disorders; and blood test false positives for syphilis.

  As used herein, a “biological sample” is obtained from a subject's body. Any suitable biological sample obtained from a subject can be used. Particularly suitable samples for use in the method of the present invention are blood samples, biopsy samples including but not limited to renal biopsies. In one embodiment, serological markers (such as ANA, CENP, Jo-1, La / SS-B, and Scl-70 markers) are obtained from a blood sample, while EC4d, PC4d, ECR1, and / or The BC4d marker is deposited on circulating blood cells.

  The blood sample is preferably treated with EDTA (ethylenediaminetetraacetic acid) to inhibit complement activity. Samples can be maintained at room temperature or stored at 4 ° C. In some embodiments, the whole blood sample can be fractionated into different components. For example, in one embodiment, red blood cells are separated from other cell types in the sample by differential centrifugation. Analysis of complement activation products bound to red blood cells (eg, EC4d and ECR1) can be performed on isolated red blood cells. In some embodiments, the white blood cells are separated from other components of the blood sample. For example, white blood cells (buffy coat) can be separated from plasma and red blood cells by centrifugation. Each type of leukocyte (eg, lymphocyte, monocyte, etc.) can be separated by the use of antibodies against known cell surface markers specific to the cell type. Antibodies against leukocyte cell surface markers are known to those skilled in the art. For example, monoclonal antibodies specific for the cell surface markers CD3, CD4, CD8, and CD19 are commercially available and can be used to select lymphocytes. Analysis of complement activation products found on the surface of leukocytes such as BC4d can be performed on the isolated leukocyte fraction. Platelet fractions can be obtained from other blood components and analyzed for platelet-bound complement activation products such as PC4d. Platelet separation can be performed by methods known in the art and includes differential centrifugation or immunoprecipitation using an antibody specific to platelets (eg, CD42b).

  The amount (eg, quantity or amount) of a particular biomarker can be determined in a sample using a variety of methods known to those skilled in the art. Such methods include, but are not limited to, flow cytometry, ELISA using erythrocytes, platelets, or leukocyte lysates (eg, lymphocyte lysates), and radioimmunoassays. In one embodiment, the amount of C4d is determined using a flow cytometry method in which measurements are performed by direct or indirect immunofluorescence using polyclonal or monoclonal antibodies specific for each molecule. Each of these molecules can be measured using a separate sample (eg, red blood cell specific fraction, leukocyte specific fraction, or platelet specific fraction) or a single sample (eg, whole blood).

  As used herein, “transformation analysis” can be any suitable mathematical operation, including but not limited to generalized models (eg, logistic regression, general additive models), multivariate analysis (eg, Discriminant analysis, principal component analysis, factor analysis), and time event “survival” analysis.

  As will be appreciated by those skilled in the art based on the teachings herein, the weighting factors can be determined by various techniques and can vary widely. In one example of determining an appropriate weighting factor, multivariate logistic regression (MLR) is performed using marker amounts found in two groups of patients, for example, those with and without SLE. There are several ways to select variables (markers) that can be used in the MLR, whereby unselected markers are excluded from the model and a weighting factor is determined for each predictive marker remaining in the model. These weighting factors are then multiplied by, for example, the amount of marker in the sample (displayed in any suitable unit such as, but not limited to, weight / volume, weight / weight, weight / precipitated cell number), for example In total, the SLE risk score can be calculated.

  In one non-limiting embodiment, some components that make up the index score (eg, ANA and exclusion markers) have a predetermined threshold / cutoff value (eg, the ANA component has a value of 0, 1, or 2). And the exclusion marker can have a value of 0 or 1). In such situations, the index may vary from positive to negative (or vice versa) due to analysis errors at the threshold decision limit of ANA or exclusion markers. Thus, in some embodiments, the components used to determine the index score are evaluated using threshold / cutoff values (eg, ANA and / or exclusion markers) and are within 20% of the threshold. An “indeterminate result” can be defined for an index if it can therefore affect the positive or negative of the index. In this embodiment, the score should be noted as uncertain.

  As used herein, “combining” includes any mathematical operation (addition / subtraction / multiplication / division, and combinations thereof) that uses markers in combination to yield a single score that can be compared to a threshold. In these methods, the amount of marker can be adjusted using an appropriate weighting factor.

  The method can use a comparison of the measured SLE risk score with a standard SLE risk score. Any suitable comparison standard can be used, including, but not limited to, a predetermined amount or range of SLE risk scores in a population of normal subjects or subjects with SLE, or otherwise described herein. A quantity or range is included. As used herein, “predetermined amount” or “predetermined range” refers to a control subject (ie, a healthy subject) or a population of subjects suffering from an autoimmune disease such as SLE or a non-SLE autoimmune disorder. It means a value or range of values that can be determined from the value or amount (eg, absolute value or concentration) of a particular SLE risk score measured. A predetermined amount or range of SLE risk scores can be selected by calculating the value or range of SLE risk score values that achieves maximum statistical significance. In some embodiments, the predetermined amount can be based on the variance of the SLE risk score obtained from the control / normal subject population. For example, the predetermined amount can be at least 2, 3, 4, or 5 standard deviations above the normal range SLE risk score. The predetermined amount or range of the SLE risk score is 50%, 60%, 70%, 75%, 80%, 85%, 90%, or 95% of patients with an SLE risk score within that amount or range It can also be determined by calculating the amount or range of SLE risk scores where the hyper has SLE.

  As used herein, “diagnose / diagnose” means to identify the presence or nature of SLE. Diagnostic methods differ in their sensitivity and specificity. The “sensitivity” of a diagnostic assay is the percentage of diseased patients who test positive (“true positive” rate). Disease patients that are not detected in the assay are “false negatives”. A subject with no disease and a negative test in the assay is said to be “true negative”. The “specificity” of a diagnostic assay is 1 minus the false positive rate, and the “false positive” rate is defined as the proportion of subjects who are positive for testing but have no disease. Certain diagnostic methods may not provide a definitive diagnosis of the condition, but it is sufficient if this method provides a positive indicator to aid diagnosis.

  “Treating” a subject for SLE or another disease based at least in part on the SLE risk score means that those subjects identified as having the potential to have SLE based on the SLE risk score are identified by the attending physician or other Means being treated as appropriate by a health care professional for SLE. Such treatments include, but are not limited to, immunosuppressants (eg, cyclophosphamide, corticosteroids, etc.) and / or disease modifying anti-rheumatic agents (DMARD, eg, methotrexate, azathioprine, leflunomide, belimumab, and Antimalarial agents such as plaquinyl and hydroxychloroquine). Disease-modifying anti-rheumatic drugs (DMARDs) are used prophylactically to reduce the incidence of relapse, a disease process, and reduce the need for steroid use, and treatment with corticosteroids when relapse occurs Is done.

  As described herein, the methods of all aspects of the invention can be performed manually or used in conjunction with an automated system or computer. For example, the method can be performed using an automated system that analyzes a blood sample of interest to determine the amount of a particular biomarker and compares it to a predetermined amount or range for this purpose. It is automatically executed by software suitable for. Computer software or computer readable media for use in the methods of the invention receives (a) data corresponding to the determination of complement component C4d deposited on the surface of red blood cells or lymphocytes (eg, B cells). And a code for receiving data corresponding to the amount of ANA, SSB (La) marker, Scl-70 marker, Jo-1 marker, CENP marker, and / or anti-dsDNA antibodies that are such samples in a biological sample (B) recovering a predetermined amount of complement component C4d deposited on the surface of such individual cells, and examples such as ANA, SSB (La) marker, Scl-70 marker, Jo-1 To recover a predetermined amount of marker, CENP marker, and / or anti-dsDNA antibody And (c) a code for determining whether an accurate SLE diagnosis can be made by comparing the data of (a) with the predetermined amount of (b) or if additional measurements of other biomarkers are required. Includes computer readable media.

  In certain embodiments of the invention, one or more predetermined amounts or predetermined ranges of biomarker amounts can be stored in a memory associated with a digital computer. Data corresponding to the determination of such samples complement C4d, ANA, SS-B (La) marker, Scl-70 marker, Jo-1 marker, CENP marker, and / or anti-dsDNA antibody (appropriate analysis) Once obtained (from the device), the digital computer can compare the measured biomarker data to one or more suitable predetermined amounts or predetermined ranges. After the comparison is performed, the digital computer can automatically calculate whether the data indicates a diagnosis of SLE.

  Thus, some embodiments of the invention are embodied by computer code executed by a digital computer. The digital computer can be a micro, mini, or large computer using a standard or professional operating system such as a Windows® based operating system. The code can be stored on any suitable computer readable medium. Examples of computer readable media include magnetic, electronic, or optical disks, tapes, sticks, chips, and the like. The code can be written in any suitable computer programming language including C, C ++, etc. by one of ordinary skill in the art.

  Thus, the present invention further comprises a non-transitory computer readable medium comprising a set of instructions for causing an apparatus for measuring the amount of a marker in a sample to perform the method of any aspect or embodiment of the present invention. Includes possible storage media. In a further aspect, the present invention provides a non-transitory method for automatically performing the method of the present invention on a computer coupled to a device for measuring the amount of an enumerated marker in a sample, such as a blood sample. Provide computer readable storage media. As used herein, “computer readable media” refers to a magnetic disk, optical disk, organic memory, and any other volatile (eg, random access memory (“RAM”)) readable by a CPU or Includes non-volatile (read only memory (“ROM”)) mass storage systems. Computer-readable media includes coordinated or interconnected computer-readable media that is present only in a processing system or distributed among a plurality of interconnected processing systems that can be local or remote to the processing system. . Any device suitable for measuring the amount of marker can be used, including, but not limited to, a flow cytometry device and a device that performs an ELISA.

  The present invention also provides a kit and test combination for diagnosing SLE. In one embodiment, the present invention provides a first test for the amount of EC4d, a second test for the amount of BD4d, an SS-B (La) marker, a Scl-70 marker, a Jo-1 marker, and a CENP marker. At least one of a third test for the amount of one or more (1, 2, 3, or all four), a fourth test for the amount of ANA, and a fifth test for the amount of anti-dsDNA antibody. Includes valid test combinations for diagnosis of SLE including 3 tests (ie all 3, 4, and 5 tests). A kit or test for measuring the amount of a particular biomer marker includes various reagents for performing the measurement according to the methods described herein. For example, in one embodiment, the kit or test includes reagents for performing immunofluorescence measurements of each biomarker, and includes monoclonal antibodies specific for one or more markers specific for the listed targets. Conjugates of fluorescent moieties and the like are included. In addition, the kit includes, for example, a buffer, a radiolabeled antibody, a colorimetric reagent, instructions for separating different cell fractions from whole blood, and a diagnosis of SLE based on a specific predetermined amount of biomarker. Are included as other materials necessary to perform this type of assay.

  In another embodiment, the kit or test includes reagents for performing other standard assays for each of the biomarkers, such as an ELISA or radioimmunoassay. In such embodiments, the kit or test is a monoclonal antibody specific to the listed target or other conjugated with an appropriate label such as an enzyme such as radioactive iodine, avidin, biotin, or peroxidase. Contains a marker. In addition, the kit includes a buffer, an antibody-conjugating enzyme substrate, instructions for separating different cell fractions from whole blood, and instructions for diagnosing SLE based on a specific predetermined amount of biomarker. Including.

  The examples and embodiments described herein are for illustrative purposes only, and various modifications or changes in light of this will be suggested to those skilled in the art, within the spirit and scope of the present application, and attached. Within the scope of the following claims. All references, publications, patents, and patent applications cited herein are incorporated by reference in their entirety for all purposes.

Examples Statements Systemic lupus erythematosus (SLE) is a chronic autoimmune disease that causes autoantibody-mediated tissue damage and life-threatening multi-organ failure (1). This heterogeneous inflammatory disease affects 161,000 to 322,000 adults in the United States, with women 9 times more affected than men (2). Furthermore, the prevalence is higher in African Americans and Hispanics than in Caucasians, and sociodemographic background predicts poor prognosis (3). The findings of SLE vary and include rash, arthritis, anemia, thrombocytopenia, serositis, nephritis, seizures, and mental abnormalities. Because these symptoms are heterogeneous, nonspecific, progressive, and often similar to other diseases, the diagnosis of SLE is complex and can be difficult for physicians. The diagnosis of SLE depends on a combination of the patient's medical history, current symptoms, and laboratory tests. The revised American College of Rheumatology Standards published in 1982 (4) requires the presence of 4 of 11 items to classify patients as having SLE, but the use of these criteria in clinical practice Are not unified (5). Among the standard clinical tests commonly used to assist in the diagnosis of SLE, the main are antinuclear antibodies (ANA) and anti-double-stranded DNA (anti-dsDNA) antibodies (6) . Nonetheless, there are limitations to ANA and anti-dsDNA antibodies, and none of these serological markers provide the appropriate balance of sensitivity and / or specificity in diagnosing SLE. It is well recognized that complement activation is central to the pathogenesis of SLE (7) and that cell-bound C4d fragments deposited on erythrocytes and B cells can facilitate the diagnosis of SLE (8-11). Yes. In addition, the possibility that CB-CAP is involved in disease activity has been established by reports (12, 13), and these measures may help the treating physician to manage SLE patients.

  We have already established the involvement of CB-CAP in the diagnosis of SLE in a large multi-center cross-sectional study (14) and our data show that excess C4d complement on red blood cells, B cells, and platelets. Body deposition, or low CR1 expression on erythrocytes, was confirmed to be generally 7-fold different in SLE compared to other diseases (14). However, a significant overlap in the expression of these markers is observed between SLE and other rheumatic diseases, so that none of the individual CB-CAPs alone can achieve the appropriate balance of clinical sensitivity and specificity. Indicated (15). In this test, anti-dsDNA was not sensitive (29.5%), but was still a specific marker (> 95%), whereas ANA was a sensitive but poorly specific marker. The present inventors relied on the high specificity (low false positive rate) of anti-dsDNA to evaluate whether the diagnostic value of CB-CAP ANA and anti-MCV would increase. In anti-dsDNA negative individuals, stepwise addition of log-normalized EC4d and BC4d markers significantly increased sensitivity while maintaining sufficient specificity. The increase in specificity further contributed by anti-MCV depends on rheumatoid arthritis (RA) (specificity for RA is 87.7% without anti-MCV versus 97.4% when combined with MCV). It was possible to select a more optimal cutoff that would increase the overall SLE sensitivity. The index score (> 0) combining the weighted sum of ANA, EC4d, BC4d with anti-MCV was sensitive to SLE (71.6%) and specific (90.1%) . Eventually, the combination of anti-DNA and index score improved clinical sensitivity from 29.5% to 80.0% compared to anti-DNA alone. This 50.5% improvement in clinical sensitivity is more valuable than a 9.6% decrease in specificity from 96.1% to 86.5%. Furthermore, when compared to the combination of anti-dsDNA and ANA, the combination of anti-dsDNA and index score improved the specificity by 30.3% (59.0% vs. 89.3%) and the sensitivity decreased only by 10% ( 89.0% vs. 79.0%).

  However, while the specificity of our diagnostic method is high for patients with RA, the specificity for other diseases including scleroderma, dermatomyositis, vasculitis, and Sjogren's syndrome is significantly less than 75%, thus The value of this diagnostic method in clinical practice was probably limited.

Methods Clinical Trial Plans and Validation Plans The performance characteristics of the multivariate index were measured in two cohorts of subjects enrolled from May 2010 to August (Cohort 1) and from June 2011 to September 2013 (Cohort 2). Established.

Study design and planning The two independent studies (Cohorts 1 and 2) were multicenter cross-sectional studies and required a single visit to the subject. A follow-up visit was not required to evaluate the performance characteristics of the diagnostic methodology. After obtaining the subject's informed consent, the following treatment was performed.
• Obtained a medical history of subjects associated with the diagnosis of any and all rheumatic conditions and reviewed details on inclusion / exclusion criteria and diagnosis of these conditions.
• Date of diagnosis was recorded for SLE and other rheumatic conditions, and specific SLE diagnostic criteria matched were documented (revised ARC criteria for SLE classification) (Tan et al., 1982; Hochberg, 1997) .
• Recorded subject demographic data (birthday, gender, race / ethnicity).
・ A urine pregnancy test using test papers was conducted for all women capable of pregnancy.
• Approximately 15 mL of patient blood was collected for analysis of CB-CAPS (EC4d and BC4d), ANA, anti-dsDNA, anti-MCV, and ENA antibodies, and blood samples were obtained under either fasting or non-fasting conditions. The sample consisted of one 7.5 mL EDTA blood collection tube (light purple cap) and one SST blood collection tube (dark red cap) that required centrifugation prior to shipping. All biological samples were sent from the testing facility to Exage Diagnostics on the next day (using the provided transport kit). Since CB-CAPS should be analyzed within 48 hours of sample collection (see variable project ID 25945 before analysis), samples are not accepted on Saturdays, so the subject was registered only from Monday to Thursday (on Thursday The shipping deadline was 10:00 am).
• Blood was collected and shipped from the facility to Exagen Diagnostics (San Diego, core facility). To maintain blindness in the analytical laboratory, the CRF disclosing the subject's diagnosis and any subject information is faxed to the Exagen clinical project manager, while the blood sample for analysis is pre-filled It was sent directly to the analytical laboratory with the subject's individual request. The results of these tests were made unavailable to investigators during the study. Samples were identified only by subject number and initials, and the analytical laboratory was not informed of the subject's specific diagnosis. Only the clinical team had access to the patient's diagnosis throughout the trial.
Red blood cells and B lymphocytes are separated and washed, fluorescently labeled with monoclonal and / or polyclonal antibodies specific for C4d, and flowed using assays validated in our clinical laboratory Analyzed by cytometry. Mean fluorescence intensity was used as an indicator of the expression level of each cell surface marker, and antibodies to dsDNA, ANA, anti-MCV, and ENA were measured using a solid phase immunoassay approved by the FDA as an in vitro diagnostic agent. The manufacturer's cutoff was used for all ELISAs except anti-MCV.

・病歴のいずれかの時点で十分に立証された１１項目の基準の４項目以上のいずれかの組み合わせがあれば、患者がＳＬＥを有している可能性がある。
・次のリウマチ障害の１つを有すると診断される：
抗リン脂質症候群（ＡＰＳ）、線維筋痛症（ＡＮＡ陽性患者のみ）、全身性硬化症、関節リウマチ、多発性筋炎、皮膚筋炎、ウェゲナー肉芽腫症、結節性多発動脈炎、クリオグロブリン血管炎、白血球破砕性血管炎、他の免疫学的血管炎、原発性シェーグレン症候群
・さらに約２００名の健常者ボランティア（除外規定参照）を登録した。 Study population selection Criteria for inclusion:
・ Read, understand and sign an informed consent form. ・ Age is over 18 years old ・ A blood sample can be obtained with consent ・ Subjects with rheumatic status fall into one of the following two categories Must be applicable.
Table 1 shows the criteria for SLE classification in Table 1 diagnosed as SLE according to the revised ACR criteria for SLE classification.

• A patient may have SLE if there is any combination of four or more of the 11 criteria well documented at any point in the medical history.
Diagnosed as having one of the following rheumatic disorders:
Antiphospholipid syndrome (APS), fibromyalgia (ana positive patients only), systemic sclerosis, rheumatoid arthritis, polymyositis, dermatomyositis, Wegener's granulomatosis, polyarteritis nodosa, cryoglobulin vasculitis, Leukocyte vasculitis, other immunological vasculitis, primary Sjogren's syndrome, and about 200 healthy volunteers (see exclusion rule) were registered.

Exclusion criteria Applicable to healthy volunteers only: cardiovascular, mental, nerve, gastrointestinal tract (eg, gastric or duodenal ulcer, inflammatory colitis, GI bleeding history), metabolism, lung (eg, asthma, COPD), kidney (renal failure) History of liver, blood, immunity, endocrine pathology (eg, hypothyroidism, diabetes), active infection, or chronic infection (hepatitis B or hepatitis C or HIV), neoplasm Clinically important concomitant morbidity based on investigator's judgment, including history of disease and / or weight loss surgery, obvious or laboratory evidence of primary immunodeficiency syndrome, pregnant or lactating women

Study Method Data based on a total of 800 subjects consisting of approximately 300 SLE, 300 non-SLE rheumatic diseases, and 200 healthy individuals collected from approximately 15 participating institutions in the United States. Planned for one cohort. Participating facilities were encouraged to recruit the same number of subjects with SLE and other subjects with rheumatic conditions to ensure a balanced sample. Blood was collected from each subject for CB-CAPS and other serological markers.

  All data and blood samples submitted to the sponsor were strictly named using the subject's ID number. Each facility was assigned a two-digit location number, and each facility was assigned a second ID number. Each facility was responsible for maintaining a list of trial numbers and associated subject names at that location. All subject identifiers were removed from the documentation and all blood samples were identified only by subject ID number and initials. In addition, the diagnosis of all subjects was hidden in the sponsor's clinical laboratory.

Statistical analysis Statistical analysis was performed using R software version 2.15.0. Statistical analysis utilized the area under the receiver operating characteristic (ROC) curve (SLE vs. non-SLE patients excluding healthy subjects) and the calculation of diagnostic sensitivity and specificity. Receiver operating characteristics were used as needed for each marker (univariate analysis) and as the output of a multivariate logistic regression equation after determination of index values. Sensitivity and specificity were calculated as an evaluation of performance. Reported performance statistics (sensitivity, specificity) and ROC AUC were calculated by cross-validation.

Clinical outcomes All 304 SLE patients and 285 patients with other rheumatic diseases (87.8% women, mean age 48 years) were eligible for analysis. The 285 group with other rheumatic diseases included RA (161 patients, 85% female, average age 59 years), systemic sclerosis (35 patients, 80% female, average age 53 years), multiple Myositis / dermatomyositis (PM / DM: 27 patients, 81% female, mean age 56 years), primary Sjogren's syndrome (33 patients, 94% female, mean age 56 years), other various rheumatic diseases 29 patients (83% female, average age 55 years) (granulomatosis with multiple vasculitis [5], fibromyalgia [13], vasculitis [10], antiphospholipid syndrome [1] Name]). A total of 205 healthy individuals (66% female, average age 41 years) were also registered (Table 2).

The characteristics of 304 patients are shown in Table 3.

Univariate analysis of markers used in SLE diagnostics Serological markers (ANA, anti-dsDNA, anti-MCV, SS-B / La, CENP, Scl-70, Smith) and all registered CB-CAP Determined on subject (n = 794). ANA (≧ 20 units) was a sensitive marker (positive 91.7%, 279 SLE patients tested), but quite nonspecific for other rheumatic diseases (52.3%) (Table 4).

Of 304 SLE patients, a total of 101 of these SLE patients were positive for anti-dsDNA test by solid phase assay (33.2%), and 97 test results were performed by Crithia Luciliae indirect immunofluorescence assay (INOVO Diagnostics). Was used to confirm positivity, which resulted in a final sensitivity of 31.9% (97/304). A total of 11 non-SLE subjects (6 RA, 2 scleroderma, 2 vasculitis, 1 healthy subject) were positive for anti-dsDNA testing by ELISA (> 301 units) Of these, 3 (2 RA, 1 scleroderma) were not confirmed positive by Crithidia assay, so 97.2% (277/285) for other diseases and healthy A specificity of 99.5% (204/205) was obtained. Anti-smith was poorly sensitive (14%), but was a very specific marker of SLE (100%). Or anti-MCV (cut-off 70 units), SS-B / La (> 10 units), Scl-70 (> 10 units), and Jo-1 (> 10 units), respectively, for rheumatoid arthritis, Sjogren's syndrome, strong Specific for dermatoses and PM / DM patients (Table 4)

As shown in Table 5, the amount of EC4d and BC4d was 7 times higher in SLE than other diseases or normal subjects. Amounts of EC4d greater than 75 units or BC4d greater than 200 units were very specific for SLE. In subjects with other diseases, only 2 out of 285 subjects (1 RA and 1 vasculitis) showed EC4d or BC4d levels exceeding 75 and 200 units, respectively (specificity 99.3%). Or a total of 48 SLE patients (16%) showed high EC4d or BC4d.
Also, EC4d levels above 12 MFI were 97% specific to distinguish between SLE and NHV, while BC4d levels above 48 units were 96% specific.

Multivariate Index Assay-Data analysis after analysis The diagnostic methodology used used a two-tier method. In the first tier, anti-dsDNA positive (> 301 units, confirmed by Crithidia), or anti-Smith positive (> 10 units, manufacturer's cutoff, SLE criteria), or high EC4d (> 75 units), or BC4d ( > 200 units) suggests SLE. In the second tier, which is determined with negative subjects in the first tier, the weighted index scores are the ANA component (ANA ≧ 20 units, ≧ 60 units), the CBCAPS component (log-normalized EC4d + BC4d), and anti-MCV (> 70). Unit, RA marker), or SS-B / La (> 10 units, Sjogren's disease marker), or CENP / Scl-70 (> 10 units, scleroderma marker), or Jo-1 (> 10 units, multiple) Cumulative specificity component consisting of positive rate of myositis / dermatomyositis). An index> 0 suggests SLE, and a two-tier combination results in overall performance characteristics. FIG. 1 summarizes the two-tier diagnostic methodology used.

Tier 1: Performance characteristics Of the 794 subjects registered, a total of 150 of these were positive test results for at least one of the first stage markers (dsDNA> 301 units confirmed by Crithidia, or Anti-Sm> 10 units, or EC4d> 75 units, or BC4d> 200 units). Of these 150 subjects, 140 had SLE (sensitivity 46.1%, 140/304), while these 9 had other rheumatic diseases (5 RA, systemic sclerosis) 1), 2 vasculitis, 1 APS, 1 normal) (96.8% specificity, 276/285). The specificity for distinguishing healthy subjects was 99.5% (204/205) (Table 6).

  Of the 140 SLE patients positive in the first tier, a total of 48 (34.3%) had positive test results with EC4d> 75 units or BC4d> 200 units. Thus, a total of 645 patients (SLE 165, other diseases 276, healthy persons 204) who tested negative in the first hierarchy were analyzed by logistic regression in the second hierarchy.

Second-tier performance characteristics Logistic regression analysis in patients with SLE (n = 165) and other diseases (n = 111) showed that the positive rate of ANA by ANA (ANA component) was log-normalized ECd4 and BC4d levels ( CBCAPS component) and anti-MCV (> 70 units), SS-B / La (> 10 units), CENP (> 10 units), Scl-70 (> 10 units), or Jo-1 (> 10 units) positive Used in conjunction with the rate of complex antibody specific components.

Two thresholds were used for the ANA component of multivariate logistic regression. This is negative ANA (ANA <20 units, in this case the ANA component was assigned a value of 0), moderately positive (ANA 20-60 units, in this case the ANA component was assigned a value of 1), and strong positive (ANA > 60 units, in this case a value of 2 was assigned to the ANA component). The positive rate of the antibody-specific component was 9.1% (15/164 positive) in SLE compared to 40.2% (111/276 subjects) in subjects with other diseases. Only three normal subjects were positive for the second tier specific component.

Table 8 reveals estimates of multivariate logistic regression in the second hierarchy. Estimates were calculated using SLE and non-SLE subjects excluding healthy individuals.

The index equation corresponding to the output of the logistic regression model is as follows.
Equation 1 Index equation in first-tier negative patients

ANA component (ANAComp): When ANA <20 units, the ANA component was assigned a value of 0. When ANA was moderately positive (ANA 20-60 units), the ANA component was assigned a value of 1. If ANA was strongly positive (≧ 60), the ANA component was assigned a value of 2.
Specificity component (Spec. Comp): all marker portions of the specificity component (anti-MCV> 70 units, SS-B / La> 10 units, CENP> 10 units, Scl-70> 10 units, or Jo-1> If 10 units) were below the respective cut-off values, the results were entered as 0. Conversely, if any of the specific component markers exceeded their respective cutoffs, the result was entered as 1. log corresponds to the natural logarithm of the net MFI of EC4d and BC4d.

Table 9 shows an example of index calculation.

The index score is -1.74 (median) for other diseases (IQ range: -2.54; -0.90) and 0.47 (median) for SLE (IQ range: -0.64; 79) (FIG. 2). For NHV, it was -1.46 (median) (IQ range: -1.17; 1.86). Table 10 reveals the median index values found in the SLE and control groups (non-SLE and NHV). Sensitivity was 63.6%, specificity for other diseases was 88.8% (245/276) for patients and 97.5% (199/204) for healthy subjects. Setting a cut-off at -2.5 units resulted in 98.7% sensitivity for SLE and 24.9% specificity for other rheumatic diseases (5.8% specificity for healthy individuals). Or setting a cutoff of +2.5 units gives 50.0% sensitivity for SLE and 96.1% specificity for other rheumatic diseases (100% specificity for healthy individuals). ).

Overall performance characteristics-Tier 1 combination The combination of Tier 1 and index score (using cut-off zero) distinguishes 80.3% (244/304) sensitivity for SLE, and SLE and other rheumatic diseases A specificity of 85.5% (236/276) was obtained. The specificity to distinguish SLE from healthy subjects was 97.1% (199/205). Table 11 shows the results of the combined two-layer analysis.

Stepwise addition of CBCAPS and specificity components to the basic model The performance characteristics of the two-tier method were also compared to CBCAP (EC4d and BC4d) and the performance characteristics obtained without adding the specificity component (basic model). The basic model consisted of a two-layer analysis with anti-dsDNA and anti-Sm in the first layer and an ANA component in the second layer. The stepwise addition of CBCAPS and specificity components is shown in Table 12. As shown in Table 13, the addition of CBCAP and specificity components improved the performance characteristics and basic model, resulting in an AUC of 0.894, 80% sensitivity, and 86% distinguishing SLE from other diseases Specificity was obtained. The antibody specific component maximizes the specificity of diagnostic methodologies for RA, primary Sjogren's syndrome, PM / DM, and systemic sclerosis. The specificity for distinguishing SLE from normal subjects was 90.2% in the basic model, 94.1% in the model with CBCAPS added, and 97.1% in all models.

Cohort analysis and comparison with conventional methods The difference in sensitivity between the two cohorts (Cohort 1 = 81% vs. Cohort 2 = 79%) was not statistically significant (p = 0.552) (overall sensitivity = 80 %). Also, the difference in specificity that distinguishes SLE from other diseases was not statistically significant (cohort 1 = 87% vs cohort 2 = 84%, p = 0.485) (overall specificity = 86%) . The inventors also compared the performance characteristics of this two-tier method with those of conventional assays (Table 14). The addition of serological markers specific for connective tissue disease (SS-B / La, Scl-70, CENP, Jo-1) improves the specificity of the diagnostic methodology from 64% to 79%. (Combined data set: p = 0.024).

Index analytical reproducibility The daily reproducibility of the index was also determined for all 19 samples from 11 patients with SLE. For patients with multiple index determinations, blood was collected at weekly intervals. These patients were not eligible for clinical validation studies (Cohort 1 and Cohort 2). The composite index of ANA, CBCAPS, and antibody specific component was measured 3 times in succession for 3 consecutive days. Table 15 clarifies daily fluctuations.

  The median standard deviation was 0.12 (range 0.03-0.26).

Definition of uncertain results Uncertain index results based on uncertain ANA and specificity components Because two of the three components that make up the index score (ANA and antibody specificity component) are associated with a cut-off (ana component is zero) The antibody specific component can have a value of 0 or 1), the index is ANA (20 units, 60 units), or each marker that forms a specific component (anti-antigen). MCV [70 units], SS-B / La [10 units], CENP [10 units], Jo-1 [10 units], or Scl-70 [10 units]) based on analysis error at the medical judgment limit May change from positive to negative (or vice versa). For example, as shown in Table 16, the index is -1.0 (case 2, SS-B) based on the difference of 2 units of SS-B / La (9 units vs. 11 units) at the judgment limit of 10 units. / La = 9 units, specificity component = 0) to +1.5 (case 1, SS-B / La = 11 units, specificity component = 1).

  From this, if the marker forming the ANA and / or specificity component is near the cut-off value (thus possibly affecting the positive or negative of the index), an uncertain result for the index must be defined. Don't be. We are uncertain when the amount of ANA is in the range of 16 to 24 units (20% CV with a 20 unit cutoff) or 48 to 72 units (20% CV with a 60 unit cutoff). The ANA component was defined. Or when the anti-MCV amount is in the range of 56 to 84 units (20% CV with a 70 unit cut-off), or SS-B / La, CENP, Scl-70, and Jo-1 are in the range of 8 to 12 units. An uncertain antibody-specific component was defined at (20% CV with a 10 unit cutoff).

As shown in Table 17, the variation in index value from positive to negative is also highly dependent on the CBCAPS component in the index equation. A decision rule based on uncertain results about the index was established. Of the 794 registered subjects, a total of 39 (4.9%) showed uncertain results.

Methods FACS measurement C4d (EC4d and BC4d) deposited on erythrocytes and B lymphocytes was measured using a validated FACS assay.

  EC4d: whole blood (50 μL) was washed with Dulbecco's phosphate buffered saline, centrifuged (800 g) for 5 minutes and the erythrocyte pellet suspended in 500 μl of 1% normal goat serum solution (Jackson Immunoresearch Laboratories, West Grove, PA) I let you. Subsequently, 10 μL of erythrocyte suspension was stained with a purified mouse monoclonal antibody against human C4d (mouse anti-human C4d, Quidel Inc, San Diego), or non-specific mouse anti-human IgG1κ antibody (MOPC-12, BD Biosciences, San Jose, CA) for 45 minutes at 4 ° C. The sample was then washed as described above. The erythrocyte pellet was suspended in a solution (25 μL) containing goat anti-mouse antibody conjugated with fluorescein isothiocyanate (FITC, Jackson ImmunoResearch Laboratories, West Grove, PA) at 4 ° C. (dark) for 45 minutes. After staining, washing and suspending in 250 μL of a cooled 1% normal goat serum solution, FACS analysis was performed for detection of C4d deposited on the cell surface.

  BC4d amount: After lysis of erythrocytes from whole blood (700 μL) using ammonium chloride based reagent (BD Pharm Lyse, BD Bioscience, San Jose, Calif.) And centrifugation (800 g for 5 minutes) The suspension was suspended in 500 μL of a% normal goat serum solution and stained with the monoclonal C4d antibody as described above (45 minutes at 2-8 ° C.). Subsequently, 25 μL of the cell suspension was stained for 45 minutes at 4 ° C. using the purified mouse monoclonal antibody or non-specific mouse anti-human IgGκ antibody against human C4d described above. Cell surface C4d staining was detected using goat anti-mouse fluorescein isothiocyanate (FITC) antibody (45 min at 2-8 ° C., dark). Using a monoclonal antibody to human CD19 conjugated with R-phycoerythrin (R-PE) (CD19 reacts with a 95 kDa type I transmembrane glycoprotein expressed at all stages of B cell differentiation and maturation) A fragment derived from C4d complement activation specific for B lymphocytes was detected.

  All FACS analyzes used a Beckman Coulter FC500 cytometer and CXP software (Beckman coulter, Brea CA). The average fluorescence intensity (MFI) of the isotype backbrand control and each complement protein (C4d) was obtained, and then the nonspecific MFI was subtracted from the specific MFI results to determine the net MFI. The median daily (5 consecutive days) coefficient of variation for low, medium and high intensity EC4d levels was established using the blood of 44 patients with rheumatic disease and found to be 3.3 to 9.6 for EC4d. % Range. For BC4d, the low, medium, and high intensity daily variability coefficients in 34 patients with rheumatic disease ranged from 5.3 to 12.1%. All FACS experiments included controls that established proper calibration, correction, and linearity of the flow cytometer.

ANA, dsDNA, and anti-MCV ELISA measurements ANA, anti-dsDNA, and anti-mutant citrullinated vimentin antibodies [anti-MCV, anti-citrullinated peptide antibodies] (16) were measured using an enzyme linked immunosorbent assay (ELISA). All ELISA methods used have been approved by the US Food and Drug Administration as safe and effective for use in in vitro diagnostics. ANA and anti-dsDNA were obtained from INOVA Diagnostics (INOVA, San Diego, Calif.), And anti-MCV was obtained from Orientec Diagnostics (Germany). The intraday and daily coefficient of variation of all methods was established in our clinical laboratory and was less than 20%. Appropriate positive and negative controls were included in all ELISA tests.

CENP, Jo-1, La / SS-B, Ro / SS-A, SCl-70, Smith's fluorescent enzyme immunoassay measurement SS-A / Ro (60 kDa, 52 kDa), SS-B / La, centromere in serum or plasma Qualitative measurements of B (CENP), Scl-70 (anti-topoisomerase I), Jo-1, and Smith anti-nuclear IgG antibodies were performed using the EliA IgG method on a Phasia 250 instrument (ThermoFisher, Uppsala, Sweden). The cutoff recommended by the manufacturer was used for each analyte. The repeatability and reproducibility of the analysis was less than 20% for all analytes in our clinical laboratory.

Statistical analysis Statistical analysis was performed using R software version 2.15.0. The receiver operating curve was used if necessary for each marker (single variable analysis) and was also used as the output of the multivariate logistic regression equation after determination of the index values. As a measure of performance, sensitivity and specificity (1-false positive rate) were calculated. Reported performance statistics (sensitivity, specificity), ROC AUC was calculated using k-fold cross-validation 10 iterations (k = 10) (reported results are average values). Furthermore, the performance characteristics were calculated by the other three validation methods (data not shown) and all were very similar to the reported performance. Additional validation models were single cross validation, average verification of split samples (2/3 for training, 1/3 for verification), and pseudo-validation (also known as resubstitution). It was. Where applicable, a series of validations were obtained by disease stratification ((Book) Clinical Prediction Models: A Practical Approach to Development, Validation and Updating. Ewout W. Steyrberg. 2009).

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Claims (15)

A method for diagnosing the likelihood of SLE in a subject at risk of SLE, comprising:
(A) Risk of SLE when the individual amount of each of the EC4d marker, BC4d marker, anti-Smith (anti-Sm) antibody, and double-stranded DNA antibody (anti-dsDNA) is less than or equal to the predetermined value of the marker A subject with a negative for SLE ,
(B) (I) an erythrocyte C4d (EC4d) marker in a biological sample obtained from a subject at risk for SLE determined to be negative for SLE in (a ) above;
(II) a B cell C4d (BC4d) marker, and (III) an antinuclear antibody (ANA),
A child calculated SLE risk score based on the respective amounts of the primary marker is,
Including
The method wherein the risk score indicates the likelihood that the subject has SLE.   The method further comprises one or more exclusion markers selected from the group consisting of SS-B (La) marker, Scl-70 marker, Jo-1 marker, and CENP marker in a biological sample obtained from the subject. 2. The method of claim 1, comprising determining an amount, wherein calculating the SLE risk score comprises calculating an SLE risk score based on the respective amount of the primary marker and one or more of the exclusion markers. Method.   The method includes determining an amount of two or more types of the exclusion marker, and calculating the risk score calculates the SLE risk score based on the amounts of the primary marker and two or more types of the exclusion marker, respectively. The method of claim 2, comprising calculating.   The method includes measuring an amount of three or more types of the exclusion marker, and the calculation of the risk score calculates the SLE risk score based on the amount of each of the primary marker and three or more types of the exclusion marker. The method of claim 2, comprising calculating.   The method includes determining amounts of all four types of the exclusion markers, and calculating the risk score calculates the SLE risk score based on amounts of the primary marker and each of four or more types of the exclusion markers. The method of claim 2, comprising calculating.   The method further comprises determining the amount of MCV marker in a biological sample obtained from the subject, wherein the calculation of the SLE risk score comprises the primary marker, one or more of the exclusion markers, and the MCV marker. 6. The method according to any one of claims 1 to 5, comprising calculating an SLE risk score based on the respective quantity.   The method according to claim 1, wherein the calculation of the SLE risk score comprises adjusting the amount of one or more primary markers by one or more transformation analyses. One or more of the transformation analyzes includes a logistic regression analysis, the logistic regression analysis comprising:
(I) adjusting the amount of each of the primary markers to create a weighted score for each primary marker;
(Ii) combining the weighted scores of each primary marker to create the SLE risk score;
The method of claim 7 comprising:   The method according to any one of claims 2 to 8, wherein the calculation of the SLE risk score comprises adjusting the amount of one or more exclusion markers by one or more transformation analyses.   10. The method of claim 9, wherein the calculation of the SLE risk score comprises adjusting the amount of all four types of exclusion markers by one or more transformation analyses. One or more of the transformation analyzes includes a logistic regression analysis, the logistic regression analysis comprising:
(I) adjusting the amount of each of the primary markers and the amount of each of the one or more types of exclusion markers to create a weighted score for the primary marker and one or more types of the exclusion markers;
(Ii) combining the weighted scores of each primary marker and one or more types of the excluded markers to create the SLE risk score;
The method according to claim 9 or 10, comprising: The SLE risk score is
(A) determining the natural logarithm of the net average fluorescence intensity of EC4d and BC4d as determined by fluorescence activated cell sorting (FACS);
(B) determining whether ANA is negative (value is 0), moderately positive (value is 1), or strong positive (value is 2) based on a predetermined threshold;
(C) Any of the exclusion markers is positive (value is 1) exceeding a predetermined threshold, or none of the exclusion markers is negative (value is 0) without exceeding the predetermined threshold To decide,
(D) combining the values determined in (a) to (c) to obtain an SLE risk score;
12. A method according to any one of claims 2 to 11, calculated by:   The determination of the amount of BC4d marker comprises measuring the amount of BC4d on the surface of B lymphocytes, and the determination of the amount of EC4d marker comprises measuring the amount of EC4d on the surface of erythrocytes. The method as described in any one of 1-12.   Determining the amount of BC4d marker comprises determining the amount of BC4d in a tissue extract containing cells or B lymphocytes, and determining the amount of EC4d marker comprises determining the amount of EC4d in a tissue extract containing cells or red blood cells. 13. A method according to any one of claims 1 to 12, comprising determining an amount.   The method according to any one of claims 1 to 14, wherein the amount of the BC4d marker and the amount of the EC4d marker are determined using an antibody specific for C4d.

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